Agenda

• Basic faceting in ggplot2

• When to free scales

• Getting data in the right form

• Plotting one variable per panel

• Design decisions with multiple variables

• Scatterplot matrices

Slides and code: https://www.github.com/jtr13/panelplots

Without faceting

library(tidyverse)
g <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point()
g

Facet on one variable with facet_wrap()

facet “on” Species

g + facet_wrap(~Species)

Faceting in ggplot2

Each panel represents one categorical group / levels of a factor (type can be factor or character or integer)

glimpse(iris)

## Observations: 150
## Variables: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5…
## $ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1…
## $ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0…
## $ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, set…

Change the layout with nrow, ncol

g + facet_wrap(~Species, ncol = 1)

Add regression lines

g + geom_smooth(method = "lm", se = FALSE) + facet_wrap(~Species)

Facet on two (categorical) variables with facet_grid()

ggplot(mtcars, aes(hp,mpg)) + geom_point() + facet_grid(cyl~gear)

Label variables (in addition to factor levels)

ggplot(mtcars, aes(hp, mpg)) + geom_point() + facet_grid(cyl~gear, labeller = label_both)

Cleveland dot plots

Note that y-axis is discrete

g <- mtcars %>% rownames_to_column("car") %>% 
  ggplot(aes(mpg, reorder(car, mpg))) + geom_point(color = "blue") + theme_bw() +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()) + ylab("")
g

Facet by gear

g + facet_grid(gear ~ ., 
               labeller = label_both)

“Free” the y scale with scales = "free_y"

g + facet_grid(gear ~ ., labeller = label_both, scales = "free_y")

Change panel heights with space = "free_y"

g + facet_grid(gear ~ ., labeller = label_both, scales = "free_y", 
               space = "free_y")

In general, do not “free” numerical scales

ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() + facet_wrap(~Species, scales = "free")

incorrect

In general, do not “free” numerical scales

ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() + facet_wrap(~Species)

correct

Data must be in the right form to facet

One column of categorical data = one faceting direction

library(ggplot2movies)
str(movies)

## Classes 'tbl_df', 'tbl' and 'data.frame':    58788 obs. of  24 variables:
##  $ title      : chr  "$" "$1000 a Touchdown" "$21 a Day Once a Month" "$40,000" ...
##  $ year       : int  1971 1939 1941 1996 1975 2000 2002 2002 1987 1917 ...
##  $ length     : int  121 71 7 70 71 91 93 25 97 61 ...
##  $ budget     : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ rating     : num  6.4 6 8.2 8.2 3.4 4.3 5.3 6.7 6.6 6 ...
##  $ votes      : int  348 20 5 6 17 45 200 24 18 51 ...
##  $ r1         : num  4.5 0 0 14.5 24.5 4.5 4.5 4.5 4.5 4.5 ...
##  $ r2         : num  4.5 14.5 0 0 4.5 4.5 0 4.5 4.5 0 ...
##  $ r3         : num  4.5 4.5 0 0 0 4.5 4.5 4.5 4.5 4.5 ...
##  $ r4         : num  4.5 24.5 0 0 14.5 14.5 4.5 4.5 0 4.5 ...
##  $ r5         : num  14.5 14.5 0 0 14.5 14.5 24.5 4.5 0 4.5 ...
##  $ r6         : num  24.5 14.5 24.5 0 4.5 14.5 24.5 14.5 0 44.5 ...
##  $ r7         : num  24.5 14.5 0 0 0 4.5 14.5 14.5 34.5 14.5 ...
##  $ r8         : num  14.5 4.5 44.5 0 0 4.5 4.5 14.5 14.5 4.5 ...
##  $ r9         : num  4.5 4.5 24.5 34.5 0 14.5 4.5 4.5 4.5 4.5 ...
##  $ r10        : num  4.5 14.5 24.5 45.5 24.5 14.5 14.5 14.5 24.5 4.5 ...
##  $ mpaa       : chr  "" "" "" "" ...
##  $ Action     : int  0 0 0 0 0 0 1 0 0 0 ...
##  $ Animation  : int  0 0 1 0 0 0 0 0 0 0 ...
##  $ Comedy     : int  1 1 0 1 0 0 0 0 0 0 ...
##  $ Drama      : int  1 0 0 0 0 1 1 0 1 0 ...
##  $ Documentary: int  0 0 0 0 0 0 0 1 0 0 ...
##  $ Romance    : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Short      : int  0 0 1 0 0 0 0 1 0 0 ...

Facet on Action column

movies %>% filter(!is.na(budget)) %>% sample_n(1000) %>% 
ggplot(aes(budget, length)) + geom_point() +
  facet_wrap(~Action)

Transform data

mymovies <- movies %>% 
  select(title, length, budget, year, mpaa, Action:Short) %>% 
  gather(key = "genre", value, Action:Short) %>% 
  filter(value == 1) %>% 
  select(-value)

head(mymovies)

## # A tibble: 6 x 6
##   title                 length budget  year mpaa  genre 
##   <chr>                  <int>  <int> <int> <chr> <chr> 
## 1 $windle                   93     NA  2002 R     Action
## 2 'A' gai waak             106     NA  1983 PG-13 Action
## 3 'A' gai waak juk jaap    101     NA  1987 PG-13 Action
## 4 'Crocodile' Dundee II    110     NA  1988 ""    Action
## 5 'Gator Bait               88     NA  1974 ""    Action
## 6 'Sheba, Baby'             90     NA  1975 ""    Action

Facet on genre

lengthorder <- mymovies %>% group_by(genre) %>% summarize(meanlength = mean(length)) %>% arrange(desc(meanlength))
mymovies %>% filter(!is.na(budget)) %>% 
  sample_n(1000) %>% 
ggplot(aes(budget/1000000, length)) + geom_point() +
  facet_wrap(~factor(genre, levels = lengthorder$genre)) + xlab("budget (in millions)") +
  geom_hline(data = lengthorder, aes(yintercept = meanlength), color = "red") + 
  ggtitle ("Length vs. Budget", subtitle = "Red line indicates mean length")

Use facets to look at distributions of numeric variables

newmovies <- movies %>% select(mpaa, year:votes) %>% gather(key = "variable", value, -mpaa) %>% 
  filter(!is.na(value))
newmovies %>% sample_n(6)

## # A tibble: 6 x 3
##   mpaa  variable      value
##   <chr> <chr>         <dbl>
## 1 R     budget   20000000  
## 2 ""    year         1985  
## 3 ""    length         71  
## 4 ""    year         2004  
## 5 ""    votes          19  
## 6 ""    rating          6.6

ggplot(newmovies, aes(value)) + geom_histogram(fill = "cornflowerblue") + facet_wrap(~variable, scales = "free")

variables become levels of the new “variable” column

Facet on mpaa and variable

ggplot(newmovies, aes(value)) + geom_histogram(fill = "cornflowerblue") + facet_grid(mpaa~variable, scales = "free") + theme_bw()

One common scale

Same technique: variables become levels of a new “variable” column

Data

byyear <- movies %>% select(title:votes) %>% 
  mutate(budget_millions = budget/1000000) %>% 
  select(-budget) %>% 
  gather(key = "variable", value, -title, -year) %>% 
  group_by(year, variable) %>% 
  summarize(mean = mean(value, na.rm = T))

glimpse(byyear)

## Observations: 452
## Variables: 3
## Groups: year [113]
## $ year     <int> 1893, 1893, 1893, 1893, 1894, 1894, 1894, 1894, 1895, 1…
## $ variable <chr> "budget_millions", "length", "rating", "votes", "budget…
## $ mean     <dbl> NaN, 1.000000, 7.000000, 90.000000, NaN, 1.000000, 4.88…

ggplot(byyear, aes(year, mean)) + geom_line() + 
  facet_wrap(~variable, ncol = 1, scales = "free_y") + ggthemes::theme_economist()

Convert numerical to categorical variables

mymovies <- mymovies %>% filter(length <= 180) %>% 
  mutate(decade = factor(round(year/10)*10))

ggplot(mymovies, aes(length)) + geom_histogram(fill = "cornflowerblue") +
  facet_wrap(~decade)

ggridges

library(ggridges)

ggplot(mymovies, aes(x=length, y=fct_rev(decade))) +
  geom_density_ridges(scale = 1.5, color = "blue", fill = "blue", alpha = .4) +
  xlab("Length (in minutes)") + 
  ylab("") +
  theme_ridges() 

Design choices with multiple variables

x-axis, y-axis, row facets, column facets

color, size, shape

Think about continuous vs. categorical variables

sales <- read_csv("sales2.csv")
glimpse(sales)

## Observations: 56,702
## Variables: 5
## $ `Order method type` <chr> "Telephone", "Telephone", "Telephone", "Tele…
## $ `Retailer type`     <chr> "Department", "Department", "Department", "D…
## $ `Product line`      <chr> "Camping", "Camping", "Camping", "Camping", …
## $ Revenue             <dbl> 0.01809251, 0.08225408, 0.02143473, 0.070400…
## $ Date                <date> 2012-03-30, 2012-03-30, 2012-03-30, 2012-03…

Start with one dimension

sales %>% group_by(Date) %>% summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev)) + geom_line() + ylab("millions $") + ggtitle("Revenue")

Consider one variable at a time

sales %>% group_by(Date, `Product line`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = fct_reorder2(`Product line`, Date, SumRev))) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year")  + ylab("millions $") + ggtitle("Revenue by Product line") +
  labs(color = "Product line")

Consider one variable at a time

sales %>% group_by(Date, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = fct_reorder2(`Order method type`, Date, SumRev))) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year")  + ylab("millions $") + ggtitle("Revenue by Order method type") + 
  labs(color = "Order method type")

Consider one variable at a time

sales %>% group_by(Date, `Retailer type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = fct_reorder2(`Retailer type`, Date, SumRev))) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year")  + ylab("millions $") + ggtitle("Revenue by Retailer type") + 
  labs(color = "Retailer type")

Add faceting (one dimension)

Reorder factor levels

sales <- sales %>% 
  mutate(`Product line` = reorder(`Product line`, Revenue, function(x) sum(x)*-1),
         `Order method type` = reorder(`Order method type`, Revenue, function(x) sum(x)*-1),
         `Retailer type` = reorder(`Retailer type`, Revenue, function(x) sum(x)*-1))

Add faceting (one dimension)

sales %>% group_by(Date, `Product line`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue))  %>% 
  ggplot(aes(Date, SumRev, color = `Order method type`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_wrap(~`Product line`) +  ylab("millions $") +
  theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on product line")

Faceting (two dimensions)

sales %>% group_by(Date, `Product line`, `Retailer type`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = `Product line`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_grid(`Retailer type`~`Order method type`) +
  theme_bw() + theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on Order method and Retailer type")

Experiment

sales %>% group_by(Date, `Product line`, `Retailer type`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = `Retailer type`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_grid(`Product line`~`Order method type`) +
  theme_bw() + theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on Order method and Product line")

Experiment

sales %>% group_by(Date, `Product line`, `Retailer type`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = `Order method type`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_grid(`Product line`~`Retailer type`) +
  theme_bw() + theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on Retailer type and Product line")

Scatterplot matrices

Can’t create with faceting in ggplot2

Options:

plot()

lattice::splom()

GGally::ggpairs()

Scatterplot matrices

plot(iris)

Scatterplot matrices

lattice::splom(iris)

Scatterplot matrices

lattice::splom(iris[,1:4], group = iris$Species,
               par.settings = list(superpose.symbol = list(pch = 16, cex = .5)), 
               axis.text.cex = .5, axis.text.col = "grey50", 
               axis.line.tck = .5, auto.key = TRUE)

Scatterplot matrices

GGally::ggpairs(iris, mapping = ggplot2::aes(color = Species))